All organisms have the capacity to detect and respond to external stimuli such as odors, pheromones, hormones, and neurotransmitters. A common feature of these signaling systems is that a sustained stimulus will often produce a transient response. These responses include specific and reproducible changes in the gene transcription profile. The reproducibility of cellular responses is remarkable given that signaling pathways operate in noisy environments, which generate large fluctuations in pathway activity. The objectives of this proposal are to understand the origins and consequences of pathway fluctuations, and in particular the role of spatial cues in pathway activation and signal propagation within the cell. Our hypothesis is that mathematical techniques developed for studying stochastic (random) processes and dynamical systems can explain the complex transient behaviors of signaling networks in a living cell. To test the hypothesis we will apply mathematical models that describe observed behaviors of the pheromone response pathway in yeast. This yeast pheromone pathway is well suited for this purpose because it is extremely well characterized, relatively simple, and can be easily manipulated genetically and pharmacologically. Additionally, we have recently developed novel experimental approaches that will allow us to accurately measure transient changes in the gene transcription profile. Success in modeling the yeast pathway will eventually lead to improved models and a deeper understanding of hormone and neurotransmitter signaling behavior in more complex organisms. This project as three aims: Aim 1: We will measure functional parameters of short-lived fluorescent proteins capable of tracking transient changes in transcription. Based on these parameters we will develop mathematical descriptions of the reporter proteins that will enable us to more accurately infer changes in pathway activation, and apply these improved methods to measure spatiotemporal behavior in Aims 2 and 3. Aim 2: We will use mathematical models to understand mechanisms that regulate transcription from a prototypical pherpmone-responsive promoter. We will first develop a deterministic model based on differential equations to investigate how the signal pathway regulates activity of the pheromone-induced transcription factor (Ste12p). The model is then recast as a stochastic model to understand the origins and consequences of pathway-specific noise. Finally, a newly designed micro fluidics device will be used to determine if the noise in pathway activation and deactivation decreases in the presence of a pheromone gradient. Aim 3: We will develop a mathematical model to describe the spatiotemporal behavior of signaling pathway components that activate Ste12p;these will include the MAP kinase FusSp and the kinase scaffold protein SteSp. This model will be used to investigate spatial aspects of pathway regulation and determine how pheromone gradients affect the spatial localization of these two key signaling proteins. Finally, the spatiotemporal model is linked to a temporal model of the pheromone-regulated promoter developed in Aim 2 to accurately predict how protein localization affects transcriptional induction over time.